The National Medical Insurance Imaging AI Recognition Competition Organizing Committee 16.6.2026 17:40:01 CEST | Globenewswire | Press release China's National Medical Imaging AI Contest Extends Innovation Invitation to ASEAN China's National Medical Imaging AI Contest Extends Innovation Invitation to ASEAN NANNING, China, June 17, 2026 (GLOBE NEWSWIRE) -- According to the National Medical Insurance Imaging AI Recognition Competition Organizing Committee, following the successful seminar held in Malaysia this May, the China Medical Insurance Imaging AI Recognition Competition arrived in Vietnam on June 10, facilitating in-depth exchanges with Vietnamese government authorities including health, social security and science and technology departments, as well as local medical institutions and enterprises. A Media Snippet accompanying this announcement is available by clicking on this link. Launched in China, a global hub for AI innovation, the contest ( https://ybystds.ybj.gxzf.gov.cn ) is the country's first national-level professional competition focused on "AI+image recognition." Jointly hosted by China's National Healthcare Security Administration and the People's Government of Guangxi Zhuang Autonomous Region, the contest is extending an invitation to teams worldwide for participation and exchange, backed by strong technical support and assistance for participation, application and commercialization of results. Guangxi borders ASEAN countries, and the China-ASEAN Expo is held annually in Nanning, the regional capital. The contest will take place in Guangxi from August to October 2026. It features eight professional tracks covering diagnostic scenarios for diseases including lung cancer, breast cancer, glioma, kidney cancer and thyroid cancer, and involves multimodal medical imaging such as CT, MRI, CTA, X-ray and ultrasound. Designed based on real clinical pathway data, the contest highlights the clinical feasibility and practical value of AI solutions. For each track, the organizers will provide medical imaging datasets reviewed and annotated by teams of medical experts. The datasets include both positive and negative cases and will be used